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Journal of Quantitative Economics

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31.01.2019 | Original Article

Inflation Forecast: Just use the Disaggregate or Combine it with the Aggregate

verfasst von: Kausik Chaudhuri, Saumitra N. Bhaduri

Erschienen in: Journal of Quantitative Economics | Ausgabe 2/2019

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Abstract

Using data from India, the paper provides three stylize facts about the inflation forecasting: (a) using disaggregate data helps to achieve gains in forecast accuracy relative to forecasting the aggregate inflation directly; (b) using weights derived from spillover index for component forecasting compared to the official weights or the criterion suggested by Bates and Granger further improves efficiency; (c) combining disaggregates along with aggregate data is beneficial for forecasting inflation. Results also highlights the fact that inclusion of too many disaggregates might result in efficiency loss in short-term forecasting but definitely results in gain for the medium-term.

Vorheriger Artikel Local Leadership and Public Good: Evidence from The National Rural Employment Guarantee Scheme in India

Nächster Artikel Evaluation of the Survey of Professional Forecasters in the Greenbook’s Loss Function

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See Fair and Shiller (1990) for US GNP; and Stock and Watson (2003) for disaggregation in macroeconomic variables across Euro area countries.

According to Bates and Granger (1969), the weight w_i is defined as \( w_{i} = \frac{{\sigma - \sigma_{i} }}{2\sigma } \) where σ_i is the standard error of the corresponding disaggregate forecasts and σ is the sum of the standard error of the disaggregate forecasts with the restriction that the sum of the weights must be unity.

The constituents are food-grain, fruit and vegetables, milk, protein, spices, other food, primary nonfood items, minerals, fuel & power, manufactured food items, beverages, textiles, wood, paper, leather, rubber, chemicals, nonmetal, basic-metal, machinery & transport.

Stock and Watson (2003) has shown that these estimates are consistent in an approximate factor model with idiosyncratic errors that are serially and cross-sectionally correlated.

Results are available on request.

Inference remains exactly the same if we use the Schwartz Information Criteria.

The contributions of the constituents are food-grain (1.1%), fruit and vegetables (1.3%), milk (10.4%), protein (5.1%), spices (2.4%), other food (1.4%), primary nonfood items (7%), minerals (1.9%), fuel and power (12.8%), manufactured food items (11.7%), beverages (2.2%), textiles (0.8%), wood (0.1%), paper (1%), leather (1.7%), rubber (1.3%), chemicals (2.9%), nonmetal (0.5%), basic-metal (4.8%), machinery (0.3%) and transport (1.9%).

We select a VAR model with lag-length 1 based on AIC criteria.

The result is available on request.

We use 31 constituents.

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Titel: Inflation Forecast: Just use the Disaggregate or Combine it with the Aggregate
verfasst von: Kausik Chaudhuri
Saumitra N. Bhaduri
Publikationsdatum: 31.01.2019
Verlag: Springer India
Erschienen in: Journal of Quantitative Economics / Ausgabe 2/2019
Print ISSN: 0971-1554
Elektronische ISSN: 2364-1045
DOI: https://doi.org/10.1007/s40953-019-00155-1

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